Spat Spatiotemporal Epidemiol - Performance of cancer cluster Q-statistics for case-control residential histories.

Tópicos

{ method(1969) cluster(1462) data(1082) }
{ chang(1828) time(1643) increas(1301) }
{ spatial(1525) area(1432) region(1030) }
{ time(1939) patient(1703) rate(768) }
{ method(2212) result(1239) propos(1039) }
{ clinic(1479) use(1117) guidelin(835) }
{ case(1353) use(1143) diagnosi(1136) }
{ risk(3053) factor(974) diseas(938) }
{ studi(1119) effect(1106) posit(819) }
{ detect(2391) sensit(1101) algorithm(908) }
{ patient(2315) diseas(1263) diabet(1191) }
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{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
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{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
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Resumo

Few investigations of health event clustering have evaluated residential mobility, though causative exposures for chronic diseases such as cancer often occur long before diagnosis. Recently developed Q-statistics incorporate human mobility into disease cluster investigations by quantifying space- and time-dependent nearest neighbor relationships. Using residential histories from two cancer case-control studies, we created simulated clusters to examine Q-statistic performance. Results suggest the intersection of cases with significant clustering over their life course, Q(i), with cases who are constituents of significant local clusters at given times, Q(it), yielded the best performance, which improved with increasing cluster size. Upon comparison, a larger proportion of true positives were detected with Kulldorf's spatial scan method if the time of clustering was provided. We recommend using Q-statistics to identify when and where clustering may have occurred, followed by the scan method to localize the candidate clusters. Future work should investigate the generalizability of these findings.

Resumo Limpo

investig health event cluster evalu residenti mobil though causat exposur chronic diseas cancer often occur long diagnosi recent develop qstatist incorpor human mobil diseas cluster investig quantifi space timedepend nearest neighbor relationship use residenti histori two cancer casecontrol studi creat simul cluster examin qstatist perform result suggest intersect case signific cluster life cours qi case constitu signific local cluster given time qit yield best perform improv increas cluster size upon comparison larger proport true posit detect kulldorf spatial scan method time cluster provid recommend use qstatist identifi cluster may occur follow scan method local candid cluster futur work investig generaliz find

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